关键词: 质子交换膜燃料电池, 剩余使用寿命预测, 哈里斯鹰优化算法, 长短期记忆神经网络, 自注意力机制

Abstract:

Proton exchange membrane fuel cells（PEMFCs） are widely used in various fields. However，their performance degradation can reduce power output and energy conversion efficiency，and shorten service life. Accurate remaining useful life（RUL） prediction of PEMFCs is crucial for system maintenance，cost reduction，and stable power supply. Based on the temporal variation trend of PEMFC power output，a RUL prediction model that integrated improved Harris Hawks Optimization（HHO） algorithm，long short-term memory（LSTM） network，and self-attention mechanism was proposed. The time-power variation curve was derived from the relationship between current and voltage data. A combination of wavelet adaptive denoising and exponential smoothing was used for decomposition，denoising，and reconstruction of time-power data. To address issues such as excessive training parameters and high computational cost of LSTM，a method combining logistic chaotic mapping with the HHO algorithm was proposed to optimize LSTM，improving training speed and prediction accuracy. Leveraging the self-attention mechanism's advantages in focusing on key information and enhancing training accuracy，the HHO-LSTM-Self-Attention prediction model was established. Experimental results showed that compared with other prediction models such as HHO-LSTM，LSTM，Sparrow Search Algorithm（SSA）-LSTM，and Particle Swarm Optimization （PSO）-LSTM，the proposed model achieved higher prediction accuracy.

Key words: proton exchange membrane fuel cell, remaining useful life prediction, Harris Hawks Optimization algorithm, long short-term memory neural network, self-attention mechanism